Purpose

This script takes a deep dive into Sentinel 2 labels for a more rigorous analysis of inconsistent band data and outliers in the filtered label dataset. Here we will determine if any more label data points should be removed from the training dataset and whether or not we can glean anything from the metadata in the outlier dataset to be able to pre-emptively toss out scenes when we go to apply the classification algorithm.

harmonize_version = "2024-04-25"
outlier_version = "2024-04-25"

S2 <- read_rds(paste0("data/labels/harmonized_SEN2_labels_", harmonize_version, ".RDS"))

Check for mis-matched band data between user data and re-pull

Just look at the data to see consistent (or inconsistent) user-pulled data and our pull, here, our user data are in “BX” format and the re-pull is in “SR_BX” format. These are steps to assure data quality if the volunteer didn’t follow the directions explicitly.

pmap(.l = list(user_band = S2_user,
               ee_band = S2_ee,
               data = list(S2),
               mission = list("SENTINEL_2")),
     .f = make_band_comp_plot)

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Handful of fliers here, we’ll compare across the user-pulled and re-pulled data for consistency.

S2_inconsistent <- S2 %>% 
  filter(B2 != SR_B2 | B3 != SR_B3 | 
           B4 != SR_B4 | B5 != SR_B5 | B6 != SR_B6 | B7 != SR_B7 | 
           B11 != SR_B11 | B12 != SR_B12)

S2_inconsistent %>% 
  group_by(class) %>% 
  summarise(n_labels = n()) %>% 
  kable()

class	n_labels
algalBloom	2
cloud	53
darkNearShoreSediment	3
lightNearShoreSediment	13
offShoreSediment	20
openWater	40
other	4
shorelineContamination	2

We should see if there is any common contributor or scene here. There were no data handling differences between the user-pull and our second pull, so we can’t dismiss the cloud differences.

S2_inconsistent %>% 
  group_by(vol_init) %>% 
  summarise(n_dates = length(unique(date)),
            n_labels = n()) %>% 
  kable()

vol_init	n_dates	n_labels
ANK	7	24
BJM	9	51
JFL	2	6
LAE	1	7
LRCP	3	37
SKS	2	9
TJB	1	3

The inconsistencies are spread across many dates, so I don’t think there is any special handling necessary here except for removing these from the dataset.

S2_filtered <- S2 %>% 
  anti_join(S2_inconsistent) %>% 
  filter(# or where any re-pulled band value is greater than 1, which isn't a valid value
         if_all(S2_ee,
                ~ . <= 1))

And plot:

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And now let’s look at the data by class:

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We aren’t actually modeling “other” (not sufficient observations to classify) or “shorelineContamination” (we’ll use this later to block areas where there is likely shoreline contamination in the AOI). Additionally, “algalBloom” is likely a processing issue, and n is too low to use that data, so we’ll drop that class as well. Finally, we’ll drop the user band values since we’re done with comparisons.

S2_for_class_analysis <- S2_filtered %>% 
  filter(!(class %in% c("other", "shorelineContamination", "algalBloom"))) %>% 
  select(-c(B2:B12))

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Outlier handling

There are statistical outliers within this dataset and they may impact the interpretation of any statistical testing we do. Let’s see if we can narrow down when those outliers and/or glean anything from the outlier data that may be applicable to the the application of the algorithm. Outliers may be a systemic issue (as in the scene is an outlier), it could be a user issue (a user may have been a bad actor), or they just might be real. This section asks those questions. The “true outliers” that we dismiss from the dataset will also be used to help aid in interpretation/application of the algorithm across the Landsat stack, so it is important to make notes of any patterns we might see in the outlier dataset.

## [1] "Classes represented in outliers:"

## [1] "darkNearShoreSediment"  "lightNearShoreSediment" "offShoreSediment"      
## [4] "openWater"

Okay, 24 outliers (>1.5*IQR) out of 999 - and they are all from non-cloud groups.

How many of these outliers are in specific scenes?

S2_out_date <- outliers %>% 
  group_by(date, vol_init) %>% 
  summarize(n_out = n())
S2_date <- S2_for_class_analysis %>% 
  filter(class != "cloud") %>% 
  group_by(date, vol_init) %>% 
  summarise(n_tot = n())
S2_out_date <- left_join(S2_out_date, S2_date) %>% 
  mutate(percent_outlier = n_out/n_tot*100) %>% 
  arrange(-percent_outlier)
S2_out_date %>% 
  kable()

date	vol_init	n_out	n_tot	percent_outlier
2022-06-23	BJM	6	24	25.000000
2023-04-11	BJM	6	30	20.000000
2019-06-06	LRCP	9	148	6.081081
2020-09-28	BJM	1	32	3.125000
2021-08-29	ANK	1	62	1.612903
2019-05-12	LRCP	1	86	1.162791

There are two images with higher outliers, let’s look at them quickly:

2022-06-23

There’s some weird stuff happening here - while this does look like some near shore sediment, I think there are also some aerosol issues with this scene. If you zoom in on other waterbodies on this date, they have a similar grey-brown sheen to them, and if you zoom in on this image, it doesn’t look… natural?

2023-04-11

This is a really cool looking scene. Lots of things going on here: 1) the end of ice break up 2) super hazy/cirrus clouds 3) snow on the ground might be affecting the SR processes. We should probably ditch this scene, but we’ll do that at the end.

Tile-level metadata

Let’s look at the tile-level metadata for the highest scenes here:

S2_out_date %>% 
  filter(percent_outlier >= 20) %>% 
  select(date, vol_init) %>% 
  left_join(., S2) %>% 
  select(date, vol_init, GENERAL_QUALITY:RADIOMETRIC_QUALITY, 
         SNOW_ICE_PERCENTAGE_mean,
         SNOW_ICE_PERCENTAGE_max,
         SNOW_ICE_PERCENTAGE_min) %>% 
  distinct() %>% 
  kable()

date	vol_init	GENERAL_QUALITY	GEOMETRIC_QUALITY	RADIOMETRIC_QUALITY	SNOW_ICE_PERCENTAGE_mean	SNOW_ICE_PERCENTAGE_max	SNOW_ICE_PERCENTAGE_min
2022-06-23	BJM	PASSED	PASSED	PASSED	0.000000	0.00000	0.000000
2023-04-11	BJM	PASSED	PASSED	PASSED	9.856787	28.13553	0.000361

Some detection of snow/ice in the April image, but since these are aggregated values across multiple tiles, hard to tell what to glean from this.

Clouds

How many of these outliers have tile-level cloud metadata with high values?

The outlier dataset contains 7 (29.2%) where the mean pixel cloud cover was > 60% and 6 (25%) where the mean pixel cirrus cover was > 20%. The filtered dataset contains 97 (9.7%) where the mean pixel cloud cover was > 60% and 47 (4.7%) where the mean pixel cirrus cover was > 20%. This is a little more helpful than in Landsat images. Higher pixel cloud cover and cirrus tend to be in outliers than in the filtered dataset.

QA Pixels

Do any of the labels have QA pixel indications of opaque cloud or cirrus?

S2_for_class_analysis %>% 
  mutate(QA = case_when(cirrus == 1 | cirrus_scl == 1 ~ "cirrus",
                        hi_prob_cloud == 1 | med_prob_cloud == 1 ~ "cloud",
                        snow_ice == 1 ~ "snow ice",
                        opaque == 1 ~ "opaque cloud",
                        cloud_shadow == 1 ~ "cloud shadow",
                        dark_pixel == 1 ~ "dark pix",
                        TRUE ~ "clear")) %>% 
  group_by(QA) %>% 
  filter(class != "cloud") %>% 
  summarize(n_tot = n()) %>% 
  kable()

QA	n_tot
cirrus	14
clear	494
opaque cloud	10

Let’s look at the cirrus and opaque cloud group to see if there is anything egregious:

S2_tot <- S2_for_class_analysis %>% 
  group_by(date, vol_init) %>% 
  summarise(n_tot_labels = n())
S2_for_class_analysis %>% 
  filter(cirrus == 1 | cirrus_scl == 1 | opaque == 1, 
         class != "cloud") %>% 
  group_by(date, vol_init) %>% 
  summarise(n_qa_flag = n()) %>% 
  left_join(S2_tot) %>%
  mutate(perc_qa_flag = round(n_qa_flag/n_tot_labels*100, 1)) %>% 
  arrange(-perc_qa_flag) %>% 
  kable()

date	vol_init	n_qa_flag	n_tot_labels	perc_qa_flag
2020-10-26	BJM	9	37	24.3
2023-04-11	BJM	12	71	16.9
2020-09-28	BJM	2	103	1.9
2019-05-12	LRCP	1	109	0.9

The top tow images here are the same from the outlier analysis. I think we’ll trust the QA bit to help with all this.

Aerosol band

Sentinel 2 doesn’t have an aerosol QA flag, but SR_B1 is aerosol, so we can look at distributions to examine this. Let’s look at the distribution across all data:

S2_for_class_analysis %>% 
  ggplot(., aes(x = SR_B1)) +
  geom_histogram(binwidth = 0.01) +
  facet_grid(class ~ .) +
  theme_bw() +
  scale_x_continuous(breaks = seq(0, max(S2_for_class_analysis$SR_B1), 0.05))

B1_summary <- S2_for_class_analysis %>% 
  group_by(class) %>% 
  summarise(fifth_B1 = quantile(SR_B1, 0.05),
            mean_B1 = mean(SR_B1),
            ninetyfifth_B1 = quantile(SR_B1, 0.95))

kable(B1_summary)

class	fifth_B1	mean_B1	ninetyfifth_B1
cloud	0.168800	0.5004647	0.860100
darkNearShoreSediment	0.011015	0.0320797	0.048465
lightNearShoreSediment	0.033250	0.0546151	0.088225
offShoreSediment	0.027685	0.0422233	0.058135
openWater	0.017870	0.0293747	0.044230

Let’s see how many of the oultiers have Rrs values higher than the 95th percentile in their class:

outliers %>% 
  left_join(B1_summary) %>% 
  filter(SR_B1 > ninetyfifth_B1, class != "cloud") %>% 
  nrow()

## [1] 13

This is about 50% of the outliers, so a pretty decent QA measure.

Training dataset implications

For the purposes of training data, we are going to throw out any labels that have qa flags associated with them

S2_training_labels <- S2_for_class_analysis %>% 
  filter(if_all(c("cirrus", "cirrus_scl", "cloud_shadow", 
                    "dark_pixel", "hi_prob_cloud", "med_prob_cloud",
                    "opaque", "snow_ice"),
                  ~ . == 0) |
           class == "cloud") %>% 
  filter(!(date %in% c("2022-06-23", "2023-04-11")))

Let’s now see how many of these have high percentile SR_B1:

S2_training_labels %>% 
  left_join(B1_summary) %>% 
  filter(SR_B1 > ninetyfifth_B1, class != "cloud") %>% 
  nrow()

## [1] 14

And make sure none of them are concentrated in a specific scene:

S2_training_labels %>% 
  left_join(B1_summary) %>% 
  filter(SR_B1 > ninetyfifth_B1, class != "cloud") %>% 
  group_by(date, vol_init) %>% 
  summarise(n_high_aero = n())

## # A tibble: 6 × 3
## # Groups:   date [6]
##   date       vol_init n_high_aero
##   <date>     <chr>          <int>
## 1 2019-05-12 LRCP               2
## 2 2019-06-06 LRCP               3
## 3 2020-09-28 BJM                1
## 4 2020-10-26 BJM                2
## 5 2020-11-30 BJM                1
## 6 2022-11-30 BJM                5

This seems totally reasonable.

Testing for inter-class differences

We do want to have an idea of how different the classes are, in regards to band data. While there are a bunch of interactions that we could get into here, for the sake of this analysis, we are going to analyze the class differences by band.

Kruskal-Wallis assumptions:

Data are non-Normal or have a skewed distribution
There must be at least two independent groups.
Data have a similar distribution across groups.
Data are independent, the groups shouldn’t have a relationship to one another
Each group should contain at least 5 observations

ANOVA assumptions:

data are distributed normally
data are independent
variance across groups is similar

We can’t entirely assert sample independence and we know that variance and distribution is different for “cloud” labels, but those data also are visibly different from the other classes.

In order to systematically test for differences between classes and be able to intepret the data, we will need to know some things about our data:

Are the data normally distributed (Shapiro-Wilkes)?
Are there outliers that may impact interpretation?
If data is non-normal, perform Kruskal-Walis test; otherwise ANOVA
if the null is rejected (and there is a difference in at least one class), perform post-hoc test for pairwise comparison (Dunn test for both)

With this workflow, most classes are statistically different - below are the cases where the pairwise comparison were not deemed statistically significant:

band	group1	group2	n1	n2	statistic	p	p.adj	p.adj.signif
SR_B2	darkNearShoreSediment	offShoreSediment	64	117	2.7190570	0.0065468	0.0654683	ns
SR_B2	darkNearShoreSediment	openWater	64	154	-1.6152259	0.1062618	1.0000000	ns
SR_B2	lightNearShoreSediment	offShoreSediment	102	117	-2.6802513	0.0073567	0.0735669	ns
SR_B3	darkNearShoreSediment	offShoreSediment	64	117	1.2549300	0.2095042	1.0000000	ns
SR_B4	darkNearShoreSediment	lightNearShoreSediment	64	102	2.4622904	0.0138053	0.1380529	ns
SR_B4	darkNearShoreSediment	offShoreSediment	64	117	-1.6410142	0.1007945	1.0000000	ns
SR_B5	darkNearShoreSediment	lightNearShoreSediment	64	102	2.0770150	0.0378002	0.3780018	ns
SR_B5	darkNearShoreSediment	offShoreSediment	64	117	-2.1130300	0.0345982	0.3459820	ns
SR_B6	darkNearShoreSediment	lightNearShoreSediment	64	102	1.4614875	0.1438817	1.0000000	ns
SR_B6	darkNearShoreSediment	offShoreSediment	64	117	-1.9932388	0.0462353	0.4623530	ns
SR_B7	darkNearShoreSediment	lightNearShoreSediment	64	102	1.3916638	0.1640242	1.0000000	ns
SR_B7	darkNearShoreSediment	offShoreSediment	64	117	-1.9394492	0.0524467	0.5244667	ns
SR_B8	darkNearShoreSediment	lightNearShoreSediment	64	102	1.3596677	0.1739351	1.0000000	ns
SR_B8	darkNearShoreSediment	offShoreSediment	64	117	-1.9968907	0.0458371	0.4583706	ns
SR_B8A	darkNearShoreSediment	lightNearShoreSediment	64	102	1.2685325	0.2046079	1.0000000	ns
SR_B8A	darkNearShoreSediment	offShoreSediment	64	117	-1.6112531	0.1071246	1.0000000	ns
SR_B11	darkNearShoreSediment	lightNearShoreSediment	64	102	0.5292842	0.5966083	1.0000000	ns
SR_B11	darkNearShoreSediment	offShoreSediment	64	117	-1.6296461	0.1031763	1.0000000	ns
SR_B11	lightNearShoreSediment	offShoreSediment	102	117	-2.4933917	0.0126529	0.1265292	ns
SR_B11	offShoreSediment	openWater	117	154	-2.4124536	0.0158456	0.1584555	ns
SR_B12	darkNearShoreSediment	lightNearShoreSediment	64	102	0.4650300	0.6419100	1.0000000	ns
SR_B12	darkNearShoreSediment	offShoreSediment	64	117	-1.4404613	0.1497369	1.0000000	ns
SR_B12	lightNearShoreSediment	offShoreSediment	102	117	-2.2006275	0.0277624	0.2776241	ns
SR_B12	offShoreSediment	openWater	117	154	-1.8261056	0.0678344	0.6783435	ns

Eep, this isn’t great, but the pairwise confusion seems concentrated in dark near shore sediment.

Let’s look at the boxplots of the non-cloud classes:

S2_training_labels_no_clouds <- S2_training_labels %>% 
  filter(class != "cloud")
pmap(.l = list(data = list(S2_training_labels_no_clouds),
               data_name = list("SENTINEL_2"),
               band = S2_ee_all),
     .f = make_class_comp_plot)

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This actually makes me feel a little better. I think there might be enough here for ML to differentiate between classes.

There are in SR_B1, 0 in SR_B2, 0 in SR_B3, 1 in SR_B4, 1 in SR_B5, 1 outliers in SR_B6, 1 in SR_B7, 1 in SR_B8, 0 in SR_B8A, 10 in SR_B11, and 13 in SR_B12.

DNSS: dark near shore sediment, LNSS: light near shore sediment, OSS: offshore sediment

Let’s zoom in on the sediment classes.

DNSS: dark near shore sediment, LNSS: light near shore sediment, OSS: offshore sediment

Export the training labels

Things to note for Sentinel 2:

any QA flagged pixel should be dissmissed before applying the algorithm
SR_B1 values greater than 0.04 for open water and dark near shore sediment, 0.05 for offshore sediment, and 0.08 for light near shore sediment may have more uncertainty associated with their labels.
bright cloud cover and snow may impact Rrs within the waterbody leading to outliers. will need to be cautious applying the algo when snow is on the ground - might consider filtering out tiles with high (>10%) snow/ice cover
higher concentrations of cirrus clouds (via tile metadata) and cloud cover may merit additional uncertainty

write_rds(S2_training_labels, paste0("data/labels/S2_labels_for_tvt_", outlier_version, ".RDS"))

Deep Dive Sentinel 2 labels for training set

ROSSyndicate

2024-04-22